Effective adaptive filtering techniques

ABSTRACT

A noise level of an input signal is determined by a noise calculation module. based on the noise level, a boundary setting module sets a reduced constellation boundary interval. This reduced constellation boundary interval is employed by an equalizer to perform adaptive equalization of the input signal.

BACKGROUND

Decision feedback equalizers can be employed in devices to correct theeffects of signal multipaths and/or inter-symbol interference (ISI) on areceived signal. However, in noisy environments, an equalizer can makeerroneous decisions in the estimation of transmitted signals. Suchdecisions can cause improper symbol feedback within the equalizer andincorrect adaptation of the equalizer's filter coefficients. As aresult, unstable equalizer performance may occur.

Currently, there is as lack of specific techniques or algorithmsdirected at attacking such harmful effects. Some equalizers can employreduced constellation boundaries. Also, it has been contemplated tosuspend the adaptation of an equalizer's filter coefficients whenconfidence in the equalizer's error signals (which can be a basis forsuch adaptation) is low. However, actual techniques based on suchconjecture do not currently exist.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing an embodiment of an apparatus.

FIG. 2 is a diagram showing an implementation embodiment that may beincluded within an equalizer.

FIG. 3 illustrates an embodiment of a logic flow.

FIGS. 4A through 4D are graphs illustrating signal constellationcharacteristics at various noise levels.

FIG. 5 is a graph illustrating a non-reduced constellation technique.

FIG. 6 is a graph illustrating a reduced constellation technique.

FIG. 7 illustrates one embodiment of a logic flow.

FIG. 8 illustrates one embodiment of a system.

DETAILED DESCRIPTION

Various embodiments may be generally directed to effective adaptivefiltering techniques. In one embodiment, for example, a noisecalculation module determines a noise level of an input signal. Based onthe noise level, a boundary setting module sets a reduced constellationboundary interval. This reduced constellation boundary interval isemployed by an equalizer (adaptive filter) to perform adaptiveequalization on the input signal. In this manner, the equalizer'sperformance is enhanced under higher noise levels (i.e., lowsignal-to-noise ratios). Other embodiments may be described and claimed.

Various embodiments may comprise one or more elements. An element maycomprise any structure arranged to perform certain operations. Eachelement may be implemented as hardware, software, or any combinationthereof, as desired for a given set of design parameters or performanceconstraints. Although an embodiment may be described with a limitednumber of elements in a certain topology by way of example, theembodiment may include more or less elements in alternate topologies asdesired for a given implementation. It is worthy to note that anyreference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment. The appearances ofthe phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

FIG. 1 illustrates one embodiment of an apparatus. In particular, FIG. 1illustrates a block diagram of an apparatus 100, which may includevarious elements. For instance, FIG. 1 shows that apparatus 100 mayinclude a receiver front-end 102 and an equalization module 104.Further, FIG. 1 shows that equalization module 104 may include a noisecalculation module 106, a noise based boundary setting module 108 (alsoreferred to as boundary setting module 108), and a noise-based adaptiveequalizer 110 (also referred to as equalizer 110). These elements may beimplemented in hardware, software, or any combination thereof. Theembodiments, however, are not limited to the elements shown in thisdrawing. For instance, embodiments may include greater or fewerelements, as well as other couplings between elements.

FIG. 1 shows that receiver front-end 102 may receive a signal 120 from acommunications medium, which may be wired or wireless. Prior to itstransmission across this communications medium, signal 120 may have beenmodulated according to one or more modulation schemes at variousfrequency range(s). Also, signal 120 may have been transmitted accordingto spread spectrum techniques, multiple access techniques, and/or othersuitable transmission techniques.

As shown in FIG. 1, receiver front-end 102 produces an input signal 122from signal 120. Input signal 122 may be a baseband signal, such as apulse amplitude modulated (PAM) signal. Thus, to produce this signal,receiver front-end 102 may include a demodulator. Moreover, receiverfront-end 102 may comprise other elements, such as down conversioncomponents to translate signal 120 from high frequency ranges into lowerfrequency ranges (e.g., an intermediate frequency (IF) and/or baseband),one or more amplifiers (e.g., a low noise amplifier and/or a variablegain amplifier) to increase the energy of signal 120, and one or morefilters to remove unwanted spectral components.

FIG. 1 shows that input signal 122 is sent to noise calculation module106 and noise-based adaptive equalizer 110. Noise calculation module 106determines or estimates a noise level, such as a signal-to-noise ratio(SNR), of signal 120. This determination may involve one or morecomputational operations. For example, a correlation calculation may beperformed on a portion of signal 122 that is known beforehand, (such asa pilot signal, a preamble sequence, etc.) and a stored or internallygenerated signal corresponding to this portion. Such a correlationcalculation yields a quantitative value having a magnitude thatindicates the noise level (e.g., SNR) of signal 120. As a result of thisdetermination, noise calculation module 106 generates a noise indicator124, which is sent to boundary setting module 108.

Based on noise indicator 124, boundary setting module 108 may determineor set a reduced constellation boundary interval 126 to be employed bynoise-based adaptive equalizer 110. Various techniques may be employedin setting the reduced boundary interval. For instance, boundary settingmodule 108 may store multiple boundary intervals, where each boundaryinterval corresponds to a particular noise level (e.g., SNR) range. Thismay be implemented, for example, as a look-up table (LUT). Thus, basedon the value of noise indicator 124, boundary setting module 108 mayselect a corresponding stored boundary interval. The embodiments,however, are not limited to such techniques.

As shown in FIG. 1, noise-based equalizer 110 receives input signal 122and generates output symbols 128, which may be either soft symbols orhard symbols. More particularly, equalizer 110 reduces undesirablecharacteristics from input signal 122, such as multipath effects and/orsignal inter-symbol interference (ISI). Equalizer 110 may be adaptive inthat its characteristics (e.g., various parameters) may change duringoperation. For instance, one or more filter coefficients, and/or variousmanners of operation may adapt based on input signal 122 and boundaryinterval 126. Such features may improve performance of equalizer 110under SNR conditions.

FIG. 2 shows an exemplary implementation embodiment 200 that may beincluded within noise-based adaptive equalizer 110. As shown in FIG. 2,this implementation may include various elements. However, theembodiments are not limited to these elements. For instance, embodimentsmay include greater or fewer elements, as well as other couplingsbetween elements.

In particular, FIG. 2 shows that implementation 200 may include a feedforward filter (FFF) 202, a feed back filter (FBF) 204, a combining node206, a slicer 208, a combining node 210, an adaptation module 212, adecision module 214, a first selection module 216, and a secondselection module 218. These elements may be implemented in hardware,software, or any combination thereof.

As shown in FIG. 2, FFF 202 receives an input signal 230 (also shown asx(n)). With reference to FIG. 1, this signal may be input signal 122.FFF 202 may be implemented as a transversal filter, tapped delay line,or other suitable arrangement. Accordingly, FIG. 2 shows FFF 202 havinga plurality of coefficients 220. Upon receipt of input signal 230, FFF202 produces a corresponding output signal 232.

Like FFF 202, FBF 204 may also be implemented as a transversal filter,tapped delay line, or other suitable arrangement. Therefore, FBF 204 mayinclude a plurality of taps 222. As shown in FIG. 2, FBF 204 may receivea feedback signal 238 and generate a corresponding output signal 240.This signal is described in greater detail below.

Signals 232 and 240 are combined (e.g., summed) at combining node 206.This produces a soft symbol stream 234 (also shown as {tilde over(y)}[k]). As shown in FIG. 2, slicer 208 receives soft symbol stream 234and converts it to a hard symbol stream 236 (also shown as ŷ[k]). Moreparticularly, slicer 208 estimates transmitted symbols corresponding toinput signal 230. This estimation is based on a set of boundaries, whichmay be specified by a reduced constellation boundary interval value 256.FIG. 2 shows that hard symbol stream 236 is sent to selection module218.

Combining node 210 generates a slicer error signal 240 by calculating adifference between hard symbol stream 236 and soft symbol stream 234.This error signal is sent to selection module 216.

Adaptation module 212 sets coefficient values for FFF 202 and FBF 204.This may involve various schemes or algorithms, such as a least meansquare (LMS) algorithm. Adaptation may be based on an error signal aswell as previous and/or current filter coefficients. Accordingly, FIG. 2shows adaptation module 212 receiving an input error signal 242 as wellas old filter coefficients 244 and 246. Based on these inputs,adaptation module 212 generates new coefficients. In particular, FIG. 2shows new coefficients 248 and 250 being sent to FFF 202 and FBF 204,respectively.

Equations (1) and (2), below, express a technique for updating FFF 202and FBF 204 using LMS adaptation.FFF_(i) [k+1]=FFF_(i) [k]−μ _(DD) ·{e[k]}·x[k−i]  (1)FBF_(j) [k+1]=FBF_(j) [k]−μ _(DD) ·{e[k]}·ŷ[k−j]  (2)

In an ideal scenario having no additive white Gaussian noise (awgn), thecoefficients of FFF 202 and FBF 204 will converge so that the calculatederrors within slicer error signal 240 will be zero, and the coefficientsconverge to a fixed set, as expressed in Equations (3) and (4).FFF_(i) [k+1]=FBF_(i) [k]  (3)FBF_(j) [k+1]=FBF_(j) [k]  (4)

However, in real situations, even though the filters have converged,their coefficients will be stable except for residual errors that aredue to awgn. As the noise level in input signal increase (as in the caseof low SNR), estimated errors slicer error signal 240 will be wrong moreoften. This may cause the coefficients of FFF 202 and FBD 204 to changein the wrong direction, which leads to instability.

Adaptation module 212 receives error signal 242 from selection module216. Based on a received control flag 254, this signal may be eithererror signal 240 or a “zero signal” 252. Selection module 218 alsoreceives control flag 254. Based on this flag, selection module 218sends either soft symbol stream 234 or hard symbol stream 236 to FBF204. Selection modules 216 and 218 may be implemented with one or moremultiplexers. However, the embodiments are not limited to such.

Details regarding control flag 254 are now provided. As shown in FIG. 2,control flag 254 is generated by decision module 214. This generation isbased on soft symbol stream 234 and reduced constellation boundaryinterval value 256 (also shown as ε). With reference to FIG. 1, boundaryinterval value 256 may be received, for example, from boundary settingmodule 108. However, the embodiments are not limited to this context.

Decision module 214 determines whether soft symbols of soft symbolstream 234 are within reduced constellation boundaries, as specified byreduced constellation boundary interval value 256. When soft symbols arewithin these boundaries, then decision module 214 sets control flag 254to designate an adaptation mode. However, when they are not, thendecision module 214 sets control flag 254 to designate a no adaptationmode.

When control flag 254 designates the adaptation mode, selection module216 sends slicer error signal 240 to FFF 202 as input error signal 242.Also, selection module 218 sends hard symbol stream 236 to FBF 204 asfeedback signal 238. However, control flag 254 designates the noadaptation mode, then selection module 216 sends zero signal 252 toadaptation module 212 as input error signal 242, and selection module218 sends soft symbol stream 234 to FBF 204 as feedback signal 238.

This feature prevents divergence from the removal of undesirablecharacteristics (e.g., multipath effects, ISI, etc.) in input signal230. For instance, FBF 204 avoids processing hard symbols having asubstantial probability of being incorrect. Also, adaptation module 212does not generate new filter coefficients. This avoids new coefficientsbeing based on incorrect hard symbol interpretations. Furtherdescription regarding these features is provided below with reference toFIGS. 4A through 6.

FIG. 2 shows that implementation 200 may output soft symbol stream 234and/or hard symbol stream 236. For instance, these symbol streams may besent to components (not shown) for further processing, such as errorcorrection decoding, deinterleaving, and/or other suitable operations.Soft symbol stream 234 may be expressed as shown below in Equation (5).$\begin{matrix}{{\overset{\sim}{y}\lbrack k\rbrack} = {{\sum\limits_{i = 1}^{N}{{{FFF}\lbrack i\rbrack} \cdot {x\left\lbrack {k - i} \right\rbrack}}} + {\sum\limits_{j = 1}^{M}{{{FBF}\lbrack j\rbrack} \cdot {{FBF\_ Memory}\left\lbrack {k - j} \right\rbrack}}}}} & (5)\end{matrix}$

Operations for the above embodiments may be further described withreference to the following figures and accompanying examples. Some ofthe figures may include a logic flow. Although such figures presentedherein may include a particular logic flow, it can be appreciated thatthe logic flow merely provides an example of how the generalfunctionality described herein may be implemented. Further, the givenlogic flow does not necessarily have to be executed in the orderpresented unless otherwise indicated. Also, the given logic flow mayinclude additional operations as well as omit certain describedoperations. In addition, the given logic flow may be implemented by ahardware, software executed by one or more processors, or anycombination thereof. The embodiments are not limited in this context.

FIG. 3 illustrates one embodiment of a logic flow. In particular, FIG. 3shows a logic flow 300, which may be representative of operationsexecuted by one or more embodiments described herein. As shown in FIG.3, a reduced constellation boundary interval, ε, is set at a block 302.This boundary interval may be based on a calculated or estimated noiselevel (e.g., SNR) associated with a received signal. With reference toFIG. 1, ε may be set, for example, by boundary setting module 108.

At a block 304, a soft symbol is received. As indicated by a block 306,it is determined whether the soft symbol falls within a reducedboundary, as specified by ε. If so, then operation proceeds to a block308. However, if not, then operation proceeds to a block 314. Theseblocks may be performed, for example, by decision module 214.

At block 308, a hard symbol is determined from the received soft symbol.This computation may be performed, for example, by slicer 208. Then, ata block 309, the hard symbol is fed through a feedback filter, such asFBF 204.

As shown in FIG. 3, a slicer error is calculated at a block 310. Asdescribed above, this may involve calculating a difference between thehard symbol and the received soft symbol. After this calculation,operation may proceed to a block 312. At this block, filter adaptationmay be performed.

As described above, operation proceeds to block 314 if the received softsymbol does not fall within a reduced boundary, as specified by ε. Atthis block, the received soft symbol is fed through a feedback filter,such as FBF 204. Also, at a block 316, the slicer error is set to zero.As an example, this may be implemented as shown in FIG. 2, whereselection module 216 selects zero signal 252 as input error signal 242to prevent the generation of new coefficients.

Thus, techniques are provided for determining when an equalizer adaptsits filter coefficients. This may be based, for example, on establishingwhen to ignore a slicer error because of its low probability of beingcorrect. Such features may advantageously enhance equalization (adaptivefiltering) under low SNR conditions. For instance, such features preventdivergence from the removal of undesirable characteristics (e.g.,multipath effects, ISI, etc.) in input signal 230. Also, such featuresadvantageously prevent divergence from the removal of undesirablecharacteristics (e.g., multipath effects, ISI, etc.) in input signal230.

The description now turns to an analytical discussion of reducedconstellation techniques. FIGS. 4A-4D illustrate various distributionsassociated with an eight level signal constellation. These drawings showexemplary received symbol distributions for an eight level vestigialsideband (8-VSB) constellation resulting from different noise levels(i.e., different SNRs). In particular, FIG. 4A shows distributions foran SNR of 30 decibels (dB), FIG. 4B shows distributions for an SNR of 20dB, FIG. 4C shows distributions for an SNR of 17 dB, and FIG. 4D showsdistributions for an SNR of 16 dB. The only impairment associated withthese symbol distributions is additive noise. Thus, other phenomena(e.g., multipath transmission, reflections, echoes, etc.) do notcontribute to these illustrated distributions.

As shown in FIGS. 4A-4D, the received symbols each have a Gaussiandistribution, centered at corresponding ideal transmitted levels. Inthis exemplary case, the ideal transmitted levels are −7, −5, −3, −1, 1,3, and 5. The variances of these distributions (exhibited as width) arefunctions of the corresponding noise levels. More particularly, as theamount of noise increases (i.e., as the SNR decreases), the variance(width) increases. Thus, the probability of making incorrect decisionsincreases with the amount of noise.

FIG. 5 provides a graph showing multiple probability density curves 502(shown as solid curves). These curves represent probabilities that areceived soft symbol ‘y’ (assuming 0<y<=2) corresponds to a transmittedsymbol value of 0. Each curve 502 corresponds to a particular noisecharacteristic (SNR). Specifically, SNR increases from curve 502 ₁ tocurve 502 ₆. The graph of FIG. 5 further shows multiple probabilitycurves 504 (shown as dotted curves). These curves representprobabilities that a received soft symbol corresponds to a transmittedsymbol value of 2. Like curves 502, each curve 504 corresponds to aparticular noise characteristic. In particular, SNR increases from curve504 ₁ to curve 504 ₆.

One technique of estimating a hard symbol, ŷ, from a soft symbol, y,(distributed, for example, according to the density curves of FIG. 5),is provided below in Expressions (6-1) and (6-2):

According to this technique, FIG. 5 shows a slicing boundary 506. Thisboundary separates contiguous soft symbol ranges 508 and 510. Thus,according to the technique of FIG. 5, when a 0 symbol is transmitted, acorrect decision is made so long as the corresponding soft symbol (e.g.,a soft symbol within soft symbol stream 234) is within soft symbol range508 (in this case, 0≦y<1), and a wrong decision is made when thereceived symbol falls outside this range.

Thus, when symbol value of 0 is transmitted, the probability of making acorrect decision is the integrated area under the corresponding SNRcurve 502 from 0 to 1. Similarly, when a symbol value of 2 istransmitted, the probability of making a correct decision is theintegrated area under the corresponding SNR curve 504 from 1 to 2. ForGaussian distributions, such probabilities are expressed below inEquations 7-1 and 7-2. $\begin{matrix}{{P\left\lbrack {{correct}\quad{decision}} \right\rbrack} = {{erf}\left( {1,\sigma_{SNR}} \right)}} & \left( {7\text{-}1} \right) \\{{{erf}\left( {z,\sigma_{SNR}} \right)} = {\frac{2}{\sigma_{SNR}\sqrt{2\quad\pi}}{\int_{0}^{z}{{\mathbb{e}}^{\frac{- x^{2}}{2\sigma_{SNR}^{2}}}{\mathbb{d}x}}}}} & \left( {7\text{-}2} \right) \\{{{{where}\quad\sigma_{SNR}} = {S*10^{- \frac{SNR}{20}}}},{and}} & \quad \\{S^{2}\quad{represents}\quad{the}\quad{signal}\quad{energy}\quad{{level}.}} & \quad\end{matrix}$

In contrast, the probability of making the wrong decision is theintegrated area under the solid curve from 1 to 2 (if the transmittedsymbols was 0) or the area under the dotted curve from 0 to 1 (if thetransmitted symbol was 2). Such probabilities are expressed below inEquations (8-1) and (8-2). $\begin{matrix}{{P\left\lbrack {{wrong}\quad{decision}} \right\rbrack} = {{cerf}\left( {1,\sigma_{SNR}} \right)}} & \left( {8\text{-}1} \right) \\{{{cerf}\left( {z,\sigma_{SNR}} \right)} = {\frac{2}{\sigma_{SNR}\sqrt{2\quad\pi}}{\int_{z}^{\infty}{{\mathbb{e}}^{\frac{- x^{2}}{2\sigma_{SNR}^{2}}}{\mathbb{d}x}}}}} & \left( {8\text{-}2} \right) \\{{{{where}\quad\sigma_{SNR}} = {S*10^{- \frac{SNR}{20}}}},{and}} & \quad \\{S^{2}\quad{represents}\quad{the}\quad{signal}\quad{energy}\quad{{level}.}} & \quad\end{matrix}$

The above expressions demonstrate that, when employing slicing boundary506, the probability of a correct decision decreases and the probabilityof a wrong decision increases as SNR decreases. This, in turn, generatesincorrect error calculations, such as in error signal 240.

FIG. 6 is a graph illustrating an exemplary reduced constellationtechnique. This graph is similar to the graph of FIG. 5. However,instead of employing slicing boundary 506, slicing boundaries 602 and604 are employed. Accordingly, FIG. 6 shows three ranges: a soft symbolrange 606 corresponding to hard symbol 0, a soft symbol range 608corresponding to hard symbol 1, and a soft symbol range 610corresponding to a undecided symbol (i.e., neither hard symbol 0 or hardsymbol 1). By employing such reduced boundaries, the probability ofincorrectly determining hard symbols may be reduced. Reduced boundariesare described in greater detail below, where the symbol ε is used torepresent particular reduced boundary values.

FIG. 7 illustrates one embodiment of a logic flow. In particular, FIG. 7illustrates a logic flow 700, which may be representative of theoperations executed by one or more embodiments described herein. Asshown in FIG. 7, this flow includes a block 702, in which a noise levelof an input signal is determined. This may involve, for example,estimating an SNR. Such estimations may include correlation operationswith known signals. Noise determination may occur at various times. Forinstance noise levels may be determined at particular or predeterminedtime intervals.

Based on the determined noise level, a reduced constellation boundaryinterval is set at a block 704. This may involve changing the reducedconstellation boundary interval in response to noise level changes.

One approach to setting the reduced constellation boundary interval mayinvolve setting it so that certain probability ratio(s) are maintained.For instance, the boundary interval may be set in a manner that keepsthe ratio of the probability of a correct decision to the probability ofan incorrect decision greater than a particular threshold. This ratio isexpressed below in Equation (9). $\begin{matrix}{\frac{P\lbrack{correct}\rbrack}{P\lbrack{incorrect}\rbrack} \geq T_{ratio}} & (9)\end{matrix}$

A further approach to setting the reduced constellation boundaryinterval may involve calculating so that certain probabilitydifference(s) are maintained. For instance, the boundary interval may beset in a manner that keeps the difference between the probability of acorrect decision and the probability of an incorrect decision greaterthan a particular threshold. This difference is expressed below inEquation (10).P[correct]−P[incorrect]≧T _(difference)   (10)

The probabilities in Equations (9) and (10) may be calculated, asexpressed below in Equations (11) and (12). $\begin{matrix}{{P\left\lbrack {{correct}\quad{decision}} \right\rbrack} = {{erf}\left( {ɛ,\sigma_{SNR}} \right)}} & (11) \\{\quad{= {\frac{2}{\sigma_{SNR}\sqrt{2\quad\pi}}{\int_{0}^{ɛ}{{\mathbb{e}}^{\frac{- x^{2}}{2\sigma_{SNR}^{2}}}{\mathbb{d}x}}}}}} & \quad \\{{P\left\lbrack {{wrong}\quad{decision}} \right\rbrack} = {{cerf}\left( {{2 - ɛ},\sigma_{SNR}} \right)}} & (12) \\{\quad{= {\frac{2}{\sigma_{SNR}\sqrt{2\quad\pi}}{\int_{2 - ɛ}^{\infty}{{\mathbb{e}}^{\frac{- x^{2}}{2\sigma_{SNR}^{2}}}{\mathbb{d}x}}}}}} & \quad\end{matrix}$

Accordingly, through these approaches, the reduced boundary intervalsthrough simulation. For instance, the reduced boundary threshold(ε_(opt)) according to the ratio approach may be determined as expressedbelow in Equation (13), while the threshold according to the differenceapproach may be determined as expressed below in Equation (14). Theembodiments, however, are not limited to these approaches.$\begin{matrix}{ɛ_{opt} = {{{\max(ɛ)}\quad{for}\quad\frac{P\lbrack{correct}\rbrack}{P\lbrack{incorrect}\rbrack}} \geq T_{ratio}}} & (13) \\{ɛ_{opt} = {{{{\max(ɛ)}\quad{for}\quad{P\lbrack{correct}\rbrack}} - {P\lbrack{incorrect}\rbrack}} \geq T_{difference}}} & (14)\end{matrix}$

With this reduced constellation boundary interval, adaptive equalizationis performed on the input signal at a block 706. This may include, forexample, generating a soft symbol associated with the input signal, anddetermining (based on the reduced constellation boundary interval andthe soft symbol) whether to adapt coefficients of one or moreequalization filters. For instance, the coefficients of the one or moreequalization filters may be adapted when the soft symbol is within areduced boundary according to the reduced constellation boundaryinterval. Such features advantageously enhance equalization (adaptivefiltering) under low SNR conditions. For instance, such features preventdivergence from the removal of undesirable characteristics (e.g.,multipath effects, ISI, etc.) in input signal 230.

FIG. 8 illustrates an embodiment of a system 800. This system may berepresentative of a system or architecture suitable for use with one ormore embodiments described herein, such as with apparatus 100,implementation 200, logic flows 300 and 700, and so forth. Accordingly,system 800 may receive signals and perform equalization according toestimated noise levels, as described herein.

As shown in FIG. 8, system 800 may include a device 802, acommunications network 804, and one or more remote devices 806. FIG. 8shows that device 802 may include the elements of FIG. 1 within areceiver module 808. Also, receiver module 808 may include a decodermodule 809. Moreover, device 802 may include a transmitter module 810, acommunications controller 812, a physical interface 814, a memory 816, auser interface 818, and a power supply 820. These elements may beimplemented in hardware, software, or any combination thereof. Moreover,these elements may be coupled according to various techniques. One suchtechnique involves employment of one or more bus interfaces.

Within receiver module 808, decoder module 809 may receive symbols fromequalization module 104 and perform decoding operations (e.g., errorcorrection decoding and deinterleaving) on these symbols. For example,decoder module 809 may receive soft symbols from equalization module 104and perform viterbi decoding and deinterleaving operations.Alternatively, decoder module 809 may receive hard symbols fromequalization module 104 and perform suitable decoding operations onthem.

Transmitter module 810 may prepare information for transmission acrossnetwork 804. For example, transmitter module 810 may include componentsfor various operations associated with the transmission of information.Examples of such operations may include encoding, modulation,upconversion, amplification, etc.

As shown in FIG. 8, communications controller 812 may be coupled toreceiver module 808 and transmitter module 810. Communicationscontroller 812 provides for the exchange of information with otherdevices across communications media, such as network 804. For instance,communications may receive such information from receiver module 808 andsend such information to transmitter module 810. This information may beprocessed and exchanged with one or more user applications (not shown).

Communications controller 812 may provide for wireless or wiredcommunications. For wireless communications, communications controller812 may include components, such as control logic to perform operationsaccording to one or more communications protocols. Thus, communicationscontroller 812 may facilitate communications across wireless networksaccording to various protocols and/or formats. For example, device 802and device(s) 806 may operate in accordance with various videotransmission standards, such as those specified by the AdvancedTelevision Systems Committee (ATSC) and/or the Digital VideoBroadcasting (DVB) organization.

In addition, these devices may operate in accordance with variouswireless local area network (WLAN) protocols, such as the IEEE 802.11series of protocols, including the IEEE 802.11a, 802.11b, 802.11e,802.11g, 802.11n, and so forth. In another example, these devices mayoperate in accordance with various wireless metropolitan area network(WMAN) mobile broadband wireless access (MBWA) protocols, such as aprotocol from the IEEE 802.16 or 802.20 series of protocols. In anotherexample, these devices may operate in accordance with various wirelesspersonal area networks (WPAN). Such networks include, for example, IEEE802.16e, Bluetooth, and the like. Also, these devices may operateaccording to Worldwide Interoperability for Microwave Access (WiMax)protocols, such as ones specified by IEEE 802.16. These protocols areprovided merely as examples. Thus, the embodiments are not limited tosuch.

Also, these devices may employ wireless cellular protocols in accordancewith one or more standards. These cellular standards may comprise, forexample, Code Division Multiple Access (CDMA), CDMA 2000, WidebandCode-Division Multiple Access (W-CDMA), Enhanced General Packet RadioService (GPRS), among other standards. The embodiments, however, are notlimited in this context.

For wired communications, communications controller 812 may includecomponents, such control logic to perform operations according to one ormore communications protocols. Examples of such communications protocolsinclude Ethernet (e.g., IEEE 802.3) protocols, integrated servicesdigital network (ISDN) protocols, public switched telephone network(PSTN) protocols, and various cable protocols. The embodiments, however,are not limited to such.

Interface 814 provides a physical coupling to resource(s) of network804. Accordingly, for wireless communications, interface 814 may includecomponents, such as one or more antennas. For wired communications,physical interface 814 may include input/output (I/O) adapters, physicalconnectors to connect the I/O adapter with a corresponding wiredcommunications medium. Examples of wired communications media mayinclude a wire, cable, metal leads, printed circuit board (PCB),backplane, switch fabric, semiconductor material, twisted-pair wire,co-axial cable, fiber optics, and so forth.

Memory 816 may store information in the form of data. For instance,memory 816 may contain one or more reduced boundary intervals arrangedin various ways (e.g., in a LUT). Also, memory 816 may storepredetermined signals and/or sequences used for correlation to estimatenoise levels. Further, memory 816 may store data conveyed duringcommunications with remote device(s) 806. Examples of such data includeinformation conveyed in signals received from network 804 and receivedby receiver module 808. For instance, with reference to FIG. 1, thisinformation may be conveyed input signal 122.

However, the embodiments are not limited in this context.

Alternatively or additionally, memory 816 may store control logic,instructions, and/or software components. These software componentsinclude instructions that can be executed by a processor. Suchinstructions may provide functionality of one or more elements in system800.

Memory 816 may be implemented using any machine-readable orcomputer-readable media capable of storing data, including both volatileand non-volatile memory. For example, memory 816 may include read-onlymemory (ROM), random-access memory (RAM), dynamic RAM (DRAM),Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM(SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory such as ferroelectric polymer memory, ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, or any other type of media suitablefor storing information. It is worthy to note that some portion or allof memory 816 may be included in other elements of system 800. Forinstance, some or all of memory 816 may be included on a same integratedcircuit or chip with such elements. Alternatively some portion or all ofmemory 816 may be disposed on a medium, for example a hard disk drive,which is external. The embodiments are not limited in this context.

User interface 818 facilitates user interaction with device 802. Thisinteraction may involve the input of information from a user and/or theoutput of information to a user. Accordingly, user interface 818 mayinclude one or more devices, such as a keypad, a touch screen, amicrophone, and/or an audio speaker. In addition, user interface 818 mayinclude a display to output information and/or render images/videoprocessed by device 802. Such images may correspond to signals receivedfrom network 804 and received by receiver module 808. For instance, withreference to FIG. 1, these images may correspond to input signal 122.Exemplary displays include liquid crystal displays (LCDs), plasmadisplays, and video displays.

Power supply 820 provides operational power to elements of device 802.Accordingly, power supply 820 may include an interface to an externalpower source, such as an alternating current (AC) source. Additionallyor alternatively, power supply 820 may include a battery. Such a batterymay be removable and/or rechargeable. However, the embodiments are notlimited to this example.

Numerous specific details have been set forth herein to provide athorough understanding of the embodiments. It will be understood bythose skilled in the art, however, that the embodiments may be practicedwithout these specific details. In other instances, well-knownoperations, components and circuits have not been described in detail soas not to obscure the embodiments. It can be appreciated that thespecific structural and functional details disclosed herein may berepresentative and do not necessarily limit the scope of theembodiments.

Various embodiments may be implemented using hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude processors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. These terms are not intendedas synonyms for each other. For example, some embodiments may bedescribed using the terms “connected” and/or “coupled” to indicate thattwo or more elements are in direct physical or electrical contact witheach other. The term “coupled,” however, may also mean that two or moreelements are not in direct contact with each other, but yet stillco-operate or interact with each other.

Some embodiments may be implemented, for example, using amachine-readable medium or article which may store an instruction or aset of instructions that, if executed by a machine, may cause themachine to perform a method and/or operations in accordance with theembodiments. Such a machine may include, for example, any suitableprocessing platform, computing platform, computing device, processingdevice, computing system, processing system, computer, processor, or thelike, and may be implemented using any suitable combination of hardwareand/or software. The machine-readable medium or article may include, forexample, any suitable type of memory unit, memory device, memoryarticle, memory medium, storage device, storage article, storage mediumand/or storage unit, for example, memory, removable or non-removablemedia, erasable or non-erasable media, writeable or re-writeable media,digital or analog media, hard disk, floppy disk, Compact Disk Read OnlyMemory (CD-ROM), Compact Disk Recordable (CD-R), Compact DiskRewriteable (CD-RW), optical disk, magnetic media, magneto-opticalmedia, removable memory cards or disks, various types of DigitalVersatile Disk (DVD), a tape, a cassette, or the like. The instructionsmay include any suitable type of code, such as source code, compiledcode, interpreted code, executable code, static code, dynamic code,encrypted code, and the like, implemented using any suitable high-level,low-level, object-oriented, visual, compiled and/or interpretedprogramming language.

Unless specifically stated otherwise, it may be appreciated that termssuch as “processing,” “computing,” “calculating,” “determining,” or thelike, refer to the action and/or processes of a computer or computingsystem, or similar electronic computing device, that manipulates and/ortransforms data represented as physical quantities (e.g., electronic)within the computing system's registers and/or memories into other datasimilarly represented as physical quantities within the computingsystem's memories, registers or other such information storage,transmission or display devices. The embodiments are not limited in thiscontext.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

1. An apparatus, comprising: a noise calculation module to determine anoise level of an input signal; a boundary setting module to set areduced constellation boundary interval based on the noise level; and anequalizer to perform adaptive equalization on the input signal inaccordance with the reduced constellation boundary interval.
 2. Theapparatus of claim 1, wherein the equalizer comprises: one or morefilters, each having a plurality of coefficients; and a decision moduleto determine, based on the reduced constellation boundary interval andone or more soft symbols associated with the input signal, whether toadapt the coefficients of the one or more filters.
 3. The apparatus ofclaim 1: wherein the equalizer comprises a slicer to generate a hardsymbol associated with the input signal; and wherein the boundarysetting module is to set the reduced constellation boundary intervalsuch that a ratio is greater than a predetermined threshold, the ratioof a probability of the one or more hard symbols being correct to aprobability of the one or more hard symbols being incorrect.
 4. Theapparatus of claim 1: wherein the equalizer comprises a slicer togenerate one or more hard symbols associated with the input signal; andwherein the boundary setting module is to set the reduced constellationboundary interval such that a difference is greater than a predeterminedthreshold, the difference between a probability of the hard symbol beingcorrect and a probability of the hard symbol being incorrect.
 5. Theapparatus of claim 1, wherein the boundary setting module comprises aplurality of stored boundary intervals for selection as the reducedconstellation boundary interval, each stored boundary intervalcorresponding to a range of noise levels.
 6. The apparatus of claim 1,comprising an antenna to receive a wireless signal corresponding to theinput signal.
 7. The apparatus of claim 1, comprising a display todisplay one or more images corresponding to the input signal.
 8. Theapparatus of claim 1, comprising memory to store information conveyed inthe input signal.
 9. The apparatus of claim 1, comprising memory tostore a plurality of boundary intervals for selection as the reducedconstellation boundary interval, each stored boundary intervalcorresponding to a range of noise levels.
 10. An apparatus, comprising:a noise calculation module to determine a noise level of an inputsignal; a boundary setting module to set a reduced constellationboundary interval based on the noise level, the boundary setting moduleincluding a plurality of stored boundary intervals for selection as thereduced constellation boundary interval, wherein each stored boundaryinterval corresponds to a range of noise levels; one or more filters,each having a plurality of coefficients; and a decision module todetermine, based on the reduced constellation boundary interval and oneor more soft symbols associated with the input signal, whether to adaptthe coefficients of the one or more filters.
 11. A method, comprising:determining a noise level of an input signal; setting a reducedconstellation boundary interval based on the noise level; and performingadaptive equalization on the input signal in accordance with the reducedconstellation boundary interval.
 12. The method of claim 11, whereinperforming adaptive equalization on the input signal in accordance withthe reduced constellation boundary interval comprises: generating a softsymbol associated with the input signal; determining, based on thereduced constellation boundary interval and the soft symbol, whether toadapt coefficients of one or more equalization filters.
 13. The methodof claim 11, wherein performing adaptive equalization on the inputsignal in accordance with the reduced constellation boundary intervalcomprises: generating a soft symbol associated with the input signal;adapting coefficients of one or more equalization filters when the softsymbol is within a reduced boundary according to the reducedconstellation boundary interval; and refraining from adaptation of thecoefficients of the one or more equalization filters when the softsymbol is outside a reduced boundary according to the reducedconstellation boundary interval.
 14. The method of claim 11, whereinsetting the reduced constellation boundary interval comprises changingthe reduced constellation boundary interval in response to a change inthe noise level.
 15. The method of claim 11, wherein determining thenoise level of the input signal is performed at predetermined timeintervals.
 16. An article comprising a machine-readable storage mediumcontaining instructions that if executed enable a system to: determine anoise level of an input signal; set a reduced constellation boundaryinterval based on the noise level; and perform adaptive equalization onthe input signal in accordance with the reduced constellation boundaryinterval.
 17. The article of claim 16, comprising instructions that ifexecuted enable the system to: generate a soft symbol associated withthe input signal; determine, based on the reduced constellation boundaryinterval and the soft symbol, whether to adapt coefficients of one ormore equalization filters.
 18. The article of claim 16, comprisinginstructions that if executed enable the system to: adapt coefficientsof one or more equalization filters when the soft symbol is within areduced boundary according to the reduced constellation boundaryinterval; and refrain from adapting the coefficients of the one or moreequalization filters when the soft symbol is outside a reduced boundaryaccording to the reduced constellation boundary interval.
 19. Thearticle of claim 16, comprising instructions that if executed enable thesystem to change the reduced constellation boundary interval in responseto a change in the noise level.
 20. The article of claim 16, comprisinginstructions that if executed enable the system to determine the noiselevel of the input signal at predetermined time intervals.